Iterative Residual Feature Refinement Network for Bit-Depth Enhancement

Bit-depth enhancement (BDE) restores high bit-depth (HBD) images from low bit-depth (LBD) ones, which has important applications. Recently, residual-optimized BDE algorithms based on convolutional neural networks (CNNs) have achieved top performance. However, they fail to use a single model to accurately recover all frequency information encoded by missing significant bits at one time on challenging large bit-depth recovery tasks. In this paper, we redefine BDE residual recovery from the perspective of image frequency characteristics. On this basis, we propose an iterative residual feature optimization strategy, which provides an implicit error correction mechanism and improves training and inference efficiency. Furthermore, we design a simple but effective iterative residual feature refinement network (IRFRN). By linking model complexity with the recovery of different frequency information, IRFRN enables a single model to simultaneously recover the missing low and high frequency information. Extensive experiments indicate that our method achieves the state-of-the-art quantitative and qualitative performance on large bit-depth recovery tasks.